For a cluster to achieve its maximum throughput, data placement and task scheduling should be handled according to the performance of the cluster nodes. In a heterogeneous cluster, each node has quite different performance, and how to evaluate nodes’ performance is a challenge issue. Normally, nodes are evaluated by benchmarks, and different benchmarks evaluate the nodes from different aspects. PageRank algorithm is used by Google to rank web sites and now it is also applied to evaluate the influence of books or users' behavior, etc. A novel PageRank based node performance evaluation algorithm is proposed to take advantage of the evaluation results from different benchmarks. Firstly, each node is evaluated by mainstream benchmarks, such as LINPACK, NPB and IOzone. Secondly, PageRank algorithm is applied to calculate the nodes’ performance according to the execution results from each benchmark. In order to use PageRank algorithm, a graph model is established, and performance vectors and probability transition matrix are also calculated. The proposed algorithm can produce comprehensive evaluation results with low computational complexity.